Cannabidiol (CBD) is a naturally occurring, non-psycho-toxic phytocannabinoid that has gained increasing attention as a popular consumer product and for its use in FDA-approved Epidiolex® (CBD oral solution) for the treatment of Lennox-Gastaut syndrome and Dravet syndrome. CBD was previously reported to be metabolized primarily by cytochrome P450 (CYP) 2C19 and CYP3A4, with minor contributions from UDP-glucuronosyltransferases. 7-Hydroxy-CBD (7-OH-CBD) is the primary active metabolite with equipotent activity compared to CBD. Given the polymorphic nature of CYP2C19, we hypothesized that variable CYP2C19 expression may lead to interindividual differences in CBD metabolism to 7-OH-CBD. The objectives of this study were to further characterize the roles of CYP enzymes in CBD metabolism, specifically to the active metabolite 7-OH-CBD, and to investigate the impact of CYP2C19 polymorphism on CBD metabolism in genotyped human liver microsomes. The results from reaction phenotyping experiments with recombinant CYP enzymes and CYP-selective chemical inhibitors indicated that both CYP2C19 and CYP2C9 are capable of CBD metabolism to 7-OH-CBD. CYP3A played a major role in CBD metabolic clearance via oxidation at sites other than the 7-position. In genotyped human liver microsomes, 7-OH-CBD formation was positively correlated with CYP2C19 activity but was not associated with CYP2C19 genotype. In a subset of single-donor human liver microsomes with moderate to low CYP2C19 activity, CYP2C9 inhibition significantly reduced 7-OH-CBD formation, suggesting that CYP2C9 may play a greater role in CBD 7hydroxylation than previously thought. Collectively, these data indicate that both CYP2C19 and CYP2C9 are important contributors in CBD metabolism to the active metabolite 7-OH-CBD.

The cannabinoids cannabidiol (CBD) and delta-9tetrahydrocannabinol (THC) undergo extensive oxidative metabolism in the liver. Although cytochromes P450 form the primary, pharmacologically active, hydroxylated metabolites of CBD and THC, less is known about the enzymes that generate the major in vivo circulating metabolites of CBD and THC, 7-carboxy-CBD and 11-carboxy-THC, respectively. The purpose of this study was to elucidate the enzymes involved in forming these metabolites.Cofactor dependence experiments with human liver subcellular fractions revealed that 7-carboxy-CBD and 11-carboxy-THC formation is largely dependent on cytosolic NAD + -dependent enzymes, with lesser contributions from NADPH-dependent microsomal enzymes. Experiments with chemical inhibitors provided evidence that 7-carboxy-CBD formation is mainly dependent on aldehyde dehydrogenases and 11-carboxy-THC formation is mediated also in part by aldehyde oxidase. This study is the first to demonstrate the involvement of cytosolic drug-metabolizing enzymes in generating major in vivo metabolites of CBD and THC and addresses a knowledge gap in cannabinoid metabolism.

Interindividual variability in drug metabolism can significantly affect drug concentrations in the body and subsequent drug response. Understanding an individual’s drug metabolism capacity is important for predicting drug exposure and developing precision medicine strategies. The goal of precision medicine is to individualize drug treatment for patients to maximize efficacy and minimize drug toxicity. While advances in pharmacogenomics have improved our understanding of how genetic variations in drug-metabolizing enzymes (DMEs) affect drug response, nongenetic factors are also known to influence drug metabolism phenotypes. This minireview discusses approaches beyond pharmacogenetic testing to phenotype DMEs—particularly the cytochrome P450 enzymes—in clinical settings. Several phenotyping approaches have been proposed: traditional approaches include phenotyping with exogenous probe substrates and the use of endogenous biomarkers; newer approaches include evaluating circulating noncoding RNAs and liquid biopsy-derived markers relevant to DME expression and function. The goals of this minireview are to 1) provide a high-level overview of traditional and novel approaches to phenotype individual drug metabolism capacity, 2) describe how these approaches are being applied or can be applied to pharmacokinetic studies, and 3) discuss perspectives on future opportunities to advance precision medicine in diverse populations. SIGNIFICANCE STATEMENT This minireview provides an overview of recent advances in approaches to characterize individual drug metabolism phenotypes in clinical settings. It highlights the integration of existing pharmacokinetic biomarkers with novel approaches; also discussed are current challenges and existing knowledge gaps. The article concludes with perspectives on the future deployment of a liquid biopsy-informed physiologically based pharmacokinetic strategy for patient characterization and precision dosing.

Sunitinib is an orally administered tyrosine kinase inhibitor associated with idiosyncratic hepatotoxicity; however, the mechanisms of this toxicity remain unclear. We have previously shown that cytochromes P450 1A2 and 3A4 catalyze sunitinib metabolic activation via oxidative defluorination leading to a chemically reactive, potentially toxic quinoneimine, trapped as a glutathione (GSH) conjugate (M5). The goals of this study were to determine the impact of interindividual variability in P450 1A and 3A activity on sunitinib bioactivation to the reactive quinoneimine and sunitinib N-dealkylation to the primary active metabolite N-desethylsunitinib (M1). Experiments were conducted in vitro using single-donor human liver microsomes and human hepatocytes. Relative sunitinib metabolite levels were measured by liquid chromatography–tandem mass spectrometry. In human liver microsomes, the P450 3A inhibitor ketoconazole significantly reduced M1 formation compared to the control. The P450 1A2 inhibitor furafylline significantly reduced defluorosunitinib (M3) and M5 formation compared to the control but had minimal effect on M1. In CYP3A5-genotyped human liver microsomes from 12 individual donors, M1 formation was highly correlated with P450 3A activity measured by midazolam 1′-hydroxylation, and M3 and M5 formation was correlated with P450 1A2 activity estimated by phenacetin O-deethylation. M3 and M5 formation was also associated with P450 3A5-selective activity. In sandwich-cultured human hepatocytes, the P450 3A inducer rifampicin significantly increased M1 levels. P450 1A induction by omeprazole markedly increased M3 formation and the generation of a quinoneimine–cysteine conjugate (M6) identified as a downstream metabolite of M5. The nonselective P450 inhibitor 1-aminobenzotriazole reduced each of these metabolites (M1, M3, and M6). Collectively, these findings indicate that P450 3A activity is a key determinant of sunitinib N-dealkylation to the active metabolite M1, and P450 1A (and potentially 3A5) activity influences sunitinib bioactivation to the reactive quinoneimine metabolite. Accordingly, modulation of P450 activity due to genetic and/or nongenetic factors may impact the risk of sunitinib-associated toxicities.

Cannabidiol (CBD) is approved for treatment of seizures associated with two forms of epilepsy that become apparent in infancy or early childhood. To consider an adult physiologically-based pharmacokinetic (PBPK) model for pediatric scaling, we assessed in vitro-derived cytochrome P450 (CYP) and uridine 5′-diph ospho-glucuronosyltransferase (UGT) enzyme contributions to CBD clearance in human. An i.v. PBPK model was constructed using CBD physicochemical properties and knowledge of disposition. The i.v. datasets were used for model building and evaluation. Oral PBPK models for CBD administered in fasted and fed states were developed using single dose oral datasets and parameters optimized from the i.v. model and evaluated with multiple dose datasets. Relative contributions of CBD metabolizing enzymes were partitioned according to in vitro studies. Clinical drug-drug interaction (DDI) studies were simulated using CBD fed state, itraconazole, fluconazole, and rifampicin PBPK models. Linear mixed effect modeling was used to estimate area under the concentration-time curve from zero to infinity (AUC 0-∞ ) perpetrator + CBD versus CBD alone. The i.v. and oral datasets used in model evaluation produced acceptable average fold error (AFE) of 1.28 and absolute AFE of 1.65. Relative contributions of drug-metabolizing enzymes to CBD clearance were proposed from in vitro data: UGT1A7 4%, UGT1A9 16%, UGT2B7 10%, CYP3A4 38%, CYP2C19 21%, and CYP2C9 11%. The simulated DDI studies using the in vitro-derived values produced AUC 0-∞ treatment ratios comparable to observed: itraconazole 1.24 versus 1.07, fluconazole 1.45 versus 1.22, and rifampicin 0.49 versus 0.69. The constructed CBD PBPK models can predict adult exposures and have potential for use in pediatrics where exposure estimates are limited.